Data Analysis using Python¶

About this short workshop¶

This workshop provides valuable insights for individuals starting their data analysis journey with Python. We will be using IPython notebooks that are compatible with both Visual Studio Code (VSCode) and Jupyter environments. To access the workshop, click on the provided link, which will open in Google Colab.

contents¶

• Part I
• Part II
• Data set

Part I¶

• Starting With Data (notebook on colab 01-starting-with-data.ipynb)
• Exercise 1 (Exercise on colab 01-excercise.ipynb)
• Working With Data (notebook on colab 02-working-with-data.ipynb)
• Exercise 2 (Exercise on colab 02-exercise.ipynb)
• Manipulating data-frame (notebook on colab 03-manipulating-data-frame.ipynb)
• Exercise 3 (Exercise on colab 03-exercise.ipynb)

Part II¶

• Summarizing (notebook on colab 04-summarizing.ipynb)
• Exercise 4 (Exercise on colab 04-exercise.ipynb)
• Merging data-frame (notebook on colab 05-merging.ipynb)
• Exercise 5 (Exercise on colab 05-exercise.ipynb)
• Visualization (notebook on colab 06-visualization.ipynb)
• Exercise 6 (Exercise on colab 06-exercise.ipynb)

Data set¶

California housing dataset¶

In this context, we make use of a tailored version of the California housing dataset for practice. This dataset is stored as a .csv file, where each row contains information about a particular strict. The columns in the dataset represent:

| Columns |
-----------------| longitude latitude
housing_median_age total_rooms total_bedrooms households median_income median_house_value

The first two rows of data/HTC.csv look line as below:

"longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income","median_house_value"
-114.310000,34.190000,15.000000,5612.000000,1283.000000,1015.000000,472.000000,1.493600,66900.000000
-114.470000,34.400000,19.000000,7650.000000,1901.000000,1129.000000,463.000000,1.820000,80100.000000

Titanic dataset¶

For this exercise, we utilize the Titanic dataset, which is stored as a .csv file. In this dataset, each row contains information about a specific passenger, while the columns represent: